Depth-Conditioned Diffusion Model for Synthetic Training Data Generation
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Solution Overview
Problem
Machine learning models, particularly deep learning neural networks, require extensive labeled image datasets for training, which is labor-intensive and impractical for unique or infrequently encountered objects, leading to inaccurate performance due to a lack of realistic and diverse training images.
Innovation Solution
A synthetic data generation system that uses depth-conditioned generative models to generate high-quality synthetic images by replacing, modifying, or augmenting objects in scenes, preserving the semantics of the original image through segmentation masks and depth information, eliminating the need for manual annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If extensive labeled image datasets are collected and annotated manually, then training data availability improves, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent uses generative models to create synthetic copies of training images automatically. Instead of manually collecting and annotating real images, the system generates synthetic images that replicate the characteristics needed for training machine learning models, thereby providing abundant training data without manual intervention
Solution Approach 2:
The system employs self-supervised learning where the generative model automatically learns from unlabelled data and generates its own training examples. The model performs self-annotation by generating synthetic images with embedded ground truth information, eliminating the need for external human annotators
2Quantity of substance
If extensive labeled image datasets are collected and annotated manually, then training data availability improves, but labor intensity increases significantly
Solution Approach 1:
The patent uses generative models to create synthetic copies of training images automatically. Instead of manually collecting and annotating real images, the system generates synthetic images that replicate the characteristics needed for training machine learning models, thereby providing abundant training data without manual intervention
Solution Approach 2:
The system employs self-supervised learning where the generative model automatically learns from unlabelled data and generates its own training examples. The model performs self-annotation by generating synthetic images with embedded ground truth information, eliminating the need for external human annotators
3Ease of manufacture
If a lack of realistic and diverse training images is present, then data collection effort decreases, but model accuracy deteriorates
Solution Approach 1:
The patent varies multiple parameters in synthetic image generation including object positions, orientations, scales, lighting conditions, and background environments. By systematically changing these parameters, the system generates diverse training images that improve model accuracy while requiring minimal data collection effort
Solution Approach 2:
The system adds dimensional diversity to training data by generating images across various virtual dimensions such as different camera angles, depths, and spatial configurations. This multi-dimensional approach creates realistically diverse training samples without physical data collection
4Productivity
If synthetic images are generated without depth information, then generation speed improves, but semantic preservation deteriorates
Solution Approach 1:
The patent incorporates depth map generation as a preliminary step before synthetic image generation. By pre-computing depth information from input images, the system ensures that generated synthetic images preserve semantic relationships and spatial structures, maintaining high generation quality without sacrificing speed
Data Source
AI summary
Synthetic data generation systems and methods are disclosed for augmenting synthetic scenes using neural networks that are conditioned on depth information. The synthetic data generation system may use a guided latent diffusion model to generate (or augment) synthetic images, which can subsequently be used to train other models to perform tasks such as object detection. Input of the model may be an image rendered by a graphic engine with coarsely rendered objects. When the image is rendered, segmentation masks may also be generated for objects in the image. The synthetic data generation system may generate a monocular depth image. During the image generation phase, the corresponding segmentation mask, depth map, and any guiding textual input serve as constraints for the denoising process. During the denoising step, the synthetic data generation system may also crop and adjust the resolution of individual objects during diffusion to enhance the results. The newly regenerated object is then blended back into the original image to produce a synthetic image.